Abstract:
—In recent years, we have witnessed rapid growth in
the application of IoT globally. IoT has found its applications
in governmental and non-governmental institutions. The
integration of a large number of electronic devices exposes
IoT technologies to various forms of cyber-attacks.
Cybercriminals have shifted their focus to the IoT as it
provides a broad network intrusion surface area. To better
protect IoT devices, we need intelligent intrusion detection
systems. This work proposes a hybrid detection system based
on Genetic Algorithm (GA) and Extreme Learning Method
(ELM). The main limitation of ELM is that the initial
parameters (weights and biases) are chosen randomly
affecting the algorithm’s performance. To overcome this
challenge, GA is used for the selection of the input weights. In
addition, the choice of activation function is key for the
optimal performance of a model. In this work, we have used
different activation functions to demonstrate the importance
of activation functions in the construction of GA-ELM. The
proposed model was evaluated using the TON_IoT network
data set. This data set is an up-to-date heterogeneous data set
that captures the sophisticated cyber threats in the IoT
environment. The results show that the GA-ELM model has
a high accuracy compared to single ELM. In addition, Relu
outperformed other activation functions, and this can be
attributed to the fact that it is known to have fast learning
capabilities and solves the challenge of vanishing gradient
witnessed in the sigmoid activation function.
